1. Field of the Invention
The present invention relates generally to classification systems and, more particularly, to systems and methods for applying machine learning to various large data sets to generate a classification model.
2. Description of Related Art
Classification models have been used to classify a variety of elements. The classification models are built from a set of training data that usually includes examples or records, each having multiple attributes or features. The objective of classification is to analyze the training data and develop an accurate model using the features present in the training data. The model is then used to classify future data for which the classification is unknown. Several classification systems have been proposed over the years, including systems based on neural networks, statistical models, decision trees, and genetic models.
One problem associated with existing classification systems has to do with the volume of training data that they are capable of handling. Existing classification systems can only efficiently handle small quantities of training data. They struggle to deal with large quantities of data, such as more than one hundred thousand features.
Accordingly, there is a need for systems and methods that are capable of generating a classification model from a large data set.